mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-29 08:34:37 +08:00
Sameer Agarwal
080d1d04bd
colPivHouseholderQR -> householderQR ldlt -> llt. The resulting performance differences are significant enough to justify switching. LAPACK's dgels routine used for solving linear least squares problems does not use pivoting either. Similarly, we are not actually using the fact that the matrix being factorized can be indefinite when using LDLT factorization, so its not clear that the performance hit is worth it. These two changes result in Eigen being able to use blocking algorithms, which for Cholesky factorization, brings the performance closer to hardware optimized LAPACK. Similarly for dense QR factorization, on intel there is a 2x speedup. Change-Id: I4459ee0fc8eb87d58e2b299dfaa9e656d539dc5e
97 lines
3.5 KiB
C++
97 lines
3.5 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
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// http://code.google.com/p/ceres-solver/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Author: sameeragarwal@google.com (Sameer Agarwal)
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#include "ceres/dense_qr_solver.h"
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#include <cstddef>
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#include "Eigen/Dense"
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#include "ceres/dense_sparse_matrix.h"
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#include "ceres/internal/eigen.h"
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#include "ceres/internal/scoped_ptr.h"
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#include "ceres/linear_solver.h"
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#include "ceres/types.h"
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#include "ceres/wall_time.h"
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namespace ceres {
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namespace internal {
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DenseQRSolver::DenseQRSolver(const LinearSolver::Options& options)
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: options_(options) {}
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LinearSolver::Summary DenseQRSolver::SolveImpl(
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DenseSparseMatrix* A,
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const double* b,
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const LinearSolver::PerSolveOptions& per_solve_options,
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double* x) {
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EventLogger event_logger("DenseQRSolver::Solve");
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const int num_rows = A->num_rows();
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const int num_cols = A->num_cols();
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if (per_solve_options.D != NULL) {
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// Temporarily append a diagonal block to the A matrix, but undo
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// it before returning the matrix to the user.
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A->AppendDiagonal(per_solve_options.D);
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}
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// rhs = [b;0] to account for the additional rows in the lhs.
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const int augmented_num_rows =
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num_rows + ((per_solve_options.D != NULL) ? num_cols : 0);
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if (rhs_.rows() != augmented_num_rows) {
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rhs_.resize(augmented_num_rows);
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rhs_.setZero();
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}
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rhs_.head(num_rows) = ConstVectorRef(b, num_rows);
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event_logger.AddEvent("Setup");
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// Solve the system.
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VectorRef(x, num_cols) = A->matrix().householderQr().solve(rhs_);
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event_logger.AddEvent("Solve");
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if (per_solve_options.D != NULL) {
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// Undo the modifications to the matrix A.
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A->RemoveDiagonal();
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}
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// We always succeed, since the QR solver returns the best solution
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// it can. It is the job of the caller to determine if the solution
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// is good enough or not.
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LinearSolver::Summary summary;
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summary.num_iterations = 1;
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summary.termination_type = TOLERANCE;
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event_logger.AddEvent("TearDown");
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return summary;
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}
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} // namespace internal
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} // namespace ceres
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